Robotic Process Mining: Vision and Challenges

Robotic process automation (RPA) is an emerging technology that allows organizations automating repetitive clerical tasks by executing scripts that encode sequences of fine-grained interactions with Web and desktop applications. Examples of clerical tasks include opening a file, selecting a field in a Web form or a cell in a spreadsheet, and copy-pasting data across fields or cells. Given that RPA can automate a wide range of routines, this raises the question of which routines should be automated in the first place. This paper presents a vision towards a family of techniques, termed robotic process mining (RPM), aimed at filling this gap. The core idea of RPM is that repetitive routines amenable for automation can be discovered from logs of interactions between workers and Web and desktop applications, also known as user interactions (UI) logs. The paper defines a set of basic concepts underpinning RPM and presents a pipeline of processing steps that would allow an RPM tool to generate RPA scripts from UI logs. The paper also discusses research challenges to realize the envisioned pipeline

Enabling Process Mining in Airbus Manufacturing : Extracting Event Logs and Discovering Processes from Complex Data

Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

Enabling Process Mining in Aircraft Manufactures: Extracting Event Logs and Discovering Processes from Complex Data

Process mining is employed by organizations to completely understand and improve their processes and to detect possible deviations from expected behavior. Process discovery uses event logs as input data, which describe the times of the actions that occur the traces. Currently, Internet-of-Things environments generate massive distributed and not always structured data, which brings about new complex scenarios since data must first be transformed in order to be handled by process min ing tools. This paper shows the success case of application of a solution that permits the transformation of complex semi-structured data of an assembly-aircraft process in order to create event logs that can be man aged by the process mining paradigm. A Domain-Specific Language and a prototype have been implemented to facilitate the extraction of data into the unified traces of an event log. The implementation performed has been applied within a project in the aeronautic industry, and promis ing results have been obtained of the log extraction for the discovery of processes and the resulting improvement of the assembly-aircraft process.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

Applications and Challenges of Task Mining: A Literature Review

Task mining is a technological innovation that combines current developments in process mining and data mining. Using task mining, the interactions of workers with their workstations can be recorded, processed, and linked with the business data of the organization. The approach can provide a holistic picture of the business processes and related tasks. Currently, there is no overview of application scenarios and the challenges of task mining. In our work, we reflect application scenarios as well as technological, legal, and organizational challenges of task mining using a structured literature review. The application areas include discovery of automation potentials, monitoring, as well as optimization of business processes. The challenges include the cleansing, collection, data protection, explainability, merging, organization, processing, and segmentation of task mining data

Fuzzy mining - adaptive process simplification based on multi-perspective metrics

Process Mining is a technique for extracting process models from execution logs. This is particularly useful in situations where people have an idealized view of reality. Real-life processes turn out to be less structured than people tend to believe. Unfortunately, traditional process mining approaches have problems dealing with unstructured processes. The discovered models are often "spaghetti-like", showing all details without distinguishing what is important and what is not. This paper proposes a new process mining approach to overcome this problem. The approach is configurable and allows for different faithfully simplified views of a particular process. To do this, the concept of a roadmap is used as a metaphor. Just like different roadmaps provide suitable abstractions of reality, process models should provide meaningful abstractions of operational processes encountered in domains ranging from healthcare and logistics to web services and public administration

Improving data preparation for the application of process mining

Immersed in what is already known as the fourth industrial revolution, automation and data exchange are taking on a particularly relevant role in complex environments, such as industrial manufacturing environments or logistics. This digitisation and transition to the Industry 4.0 paradigm is causing experts to start analysing business processes from other perspectives. Consequently, where management and business intelligence used to dominate, process mining appears as a link, trying to build a bridge between both disciplines to unite and improve them. This new perspective on process analysis helps to improve strategic decision making and competitive capabilities. Process mining brings together data and process perspectives in a single discipline that covers the entire spectrum of process management. Through process mining, and based on observations of their actual operations, organisations can understand the state of their operations, detect deviations, and improve their performance based on what they observe. In this way, process mining is an ally, occupying a large part of current academic and industrial research. However, although this discipline is receiving more and more attention, it presents severe application problems when it is implemented in real environments. The variety of input data in terms of form, content, semantics, and levels of abstraction makes the execution of process mining tasks in industry an iterative, tedious, and manual process, requiring multidisciplinary experts with extensive knowledge of the domain, process management, and data processing. Currently, although there are numerous academic proposals, there are no industrial solutions capable of automating these tasks. For this reason, in this thesis by compendium we address the problem of improving business processes in complex environments thanks to the study of the state-of-the-art and a set of proposals that improve relevant aspects in the life cycle of processes, from the creation of logs, log preparation, process quality assessment, and improvement of business processes. Firstly, for this thesis, a systematic study of the literature was carried out in order to gain an in-depth knowledge of the state-of-the-art in this field, as well as the different challenges faced by this discipline. This in-depth analysis has allowed us to detect a number of challenges that have not been addressed or received insufficient attention, of which three have been selected and presented as the objectives of this thesis. The first challenge is related to the assessment of the quality of input data, known as event logs, since the requeriment of the application of techniques for improving the event log must be based on the level of quality of the initial data, which is why this thesis presents a methodology and a set of metrics that support the expert in selecting which technique to apply to the data according to the quality estimation at each moment, another challenge obtained as a result of our analysis of the literature. Likewise, the use of a set of metrics to evaluate the quality of the resulting process models is also proposed, with the aim of assessing whether improvement in the quality of the input data has a direct impact on the final results. The second challenge identified is the need to improve the input data used in the analysis of business processes. As in any data-driven discipline, the quality of the results strongly depends on the quality of the input data, so the second challenge to be addressed is the improvement of the preparation of event logs. The contribution in this area is the application of natural language processing techniques to relabel activities from textual descriptions of process activities, as well as the application of clustering techniques to help simplify the results, generating more understandable models from a human point of view. Finally, the third challenge detected is related to the process optimisation, so we contribute with an approach for the optimisation of resources associated with business processes, which, through the inclusion of decision-making in the creation of flexible processes, enables significant cost reductions. Furthermore, all the proposals made in this thesis are validated and designed in collaboration with experts from different fields of industry and have been evaluated through real case studies in public and private projects in collaboration with the aeronautical industry and the logistics sector

Behavioral-based algorithms for process model simplification

The analysis of processes, either by Business Process Management (BPM) or Process Mining (PM) techniques, has become a must for every organization in order to improve their performance. The role of the process model, a diagrammatic representation of the process, is crucial in most of the BPM and PM phases. During past years, the amount of process-related data that has been gathered by information systems has greatly increased. With more information and behavior related to the processes being recorded, the apparition of complex process models ---with hundreds of edges and activities--- has become more common, hindering the analysis of the process during most stages of BPM and PM. For this reason, the simplification of complex process models is a promising research field that can help the analysis of complex processes